Recent News

One of the last pieces that I read as I was leaving Williamsburg last summer was an column by Tom Friedman that captured many of the the central themes for our course this fall. The central idea of the article is certainly one that resonates with me: the increase in the power of computer technology generated by robotics, artificial intelligence and “the internet of things” has created a world where every decent job demands more skill AND a stronger commitment to self-directed, lifelong learning.

Friedman quotes the C.E.O of Intel in his belief that his grandchildren won’t drive their own cars. History has shown the danger of putting too much faith in the predictions by the managers of technology companies, but it certainly seems possible that we’ll see fleets of autonomous vehicles in the next couple of decades. Those fleets will create good jobs for some engineers but they’ll also drive massive disruptions for those who drive trucks or cabs, sell lability insurance or own downtown parking lots.

The main enabler for the move to autonomous vehicles is the exponential increase in power and decreasing cost of computer power. The next generation of computer chips will allow car manufacturers to shrink the brain of a self-driving car from “something that fills the whole trunk to a small box under the front seat”. The result, as Friedman notes, is “a world where we can analyze, prophesize and optimize with a precision unknown in human history”

That ability to analyze, predict and optimize will infuse most jobs in the developed world as computer power increases. (We need to constantly remind ourself that if combined computer capability doubles every year, we’re looking at increases of 100 times plus over the next eight years.) When many of us think of the oil industry, we think of roughnecks, roustabouts and riggers on drilling platforms in the Gulf. That’s only part of the story as Friedman reminds us in his description of the the control room of Devon Energy in Oklahoma City–a “half a floor of computer screens displaying the data coming out of every well Devon is drilling around the world…if you’re working on a Devon oil rig today, you’re hold a computer, not just an oily wrench.”

The underlying reality for oil workers is that that they will need be be able to use–the computer and the wrench and they’ll need to manage their learning to keep with the changes in the computing technology. The wrench may not change much, but it’s pretty certain that the computer will be telling workers what to loosen or tighten and when to do it.

Friedman also describes how a College Board study that showed how reshaping some of our most entrenched institutions can foster self-directed, lifelong learning.

“We analyzed 250,000 students from the high school graduating class of 2017 who took the new PSAT and then the new SAT,” College Board president David Coleman told me. “Students who took advantage of their PSAT results to launch **their own free personalized improvement practice** through Khan Academy advanced dramatically: 20 hours of practice was associated with an average 115-point increase from the PSAT to the SAT — double the average gain among students who did not…

Practice advances all students without respect to high school G.P.A., gender, race and ethnicity or parental education. And it’s free. Our aim is to transform the SAT into an invitation for students to own their future.”

As we’ve been moving forward with our course this fall, we’ve been looking at how the lessons of industries as diverse as oil drilling and long-haul trucking can shape our personal and professional approaches to learning. For me, Friedman gets it right when he notes that, “And that means: More is now on you. And that means self-motivation to learn and keep learning becomes the most important life skill.”

In this interview with Cathy Engelbert, CEO of Deloitte US, Tom Friedman expands on some of the central themes of this course, which I’ve written about earlier. (The Deloitte Center for the Edge is home one of my favorite education writers, John Seely Brown.) These documents provide a good starting place for considering the interactions between technology, work and education.

For me, one of the most striking claims in this article was the statistic showing that 94% of new employment between 2005 and 2015 came from alternative work arrangements–such as the gig, or freelance, and off-balance-sheet kinds of work (1). Our students will be living in a world where work is being disconnected from traditional jobs and more and more jobs are being disconnected from companies. The companies of the past are becoming the “platforms” of the future.

The idea of companies becoming platforms certainly sounds like Silicon Valley technobabble, but if most of the new jobs in the future really will be “alternative work arrangements,” we need a new way of being “college and career ready.” And not just for our students. Our own jobs are just as likely to be disrupted by these types of innovations as any other industry.

A company becomes a platform when it no longer controls either the jobs or the assets needed to perform a job. For example, Marriott is a traditional employer–owning hotels and hiring people to staff them, but AirBB is a platform that owns no properties and hires no staff. Moreover, Uber is a platform in that owns no cars and hires no drivers. Facebook is a media platform with no writers and Google makes billions searching web sites that others create.(2)

In a world where livelihoods depend on workers connecting directly with customers, via highly competitive platforms, workers will need very different skills than they would to fill traditional jobs. Regardless of the “product” they offer, successful individuals in the gig economy will need to be marketers, bookkeepers, and customer service specialists; these are roles that employees who worked for companies never had to worry about in the past. There were staff specialists who worried full-time those things.

Perhaps most importantly, people working in the gig economy will also have to be experts in managing their own ongoing education. Unlike “traditional” employers, platforms provide none of the support and structures typically provided by corporate training and development departments. When there is a need to learn something new, the onus will be entirely on you.

If it’s any consolation to those of you who find yourself thrust into the gig economy, many workers in traditional jobs are losing their support for training as well. As an article in the Harvard Business Review stated, credible data on what businesses spend on training are scarce, but clearly investments in training and development are the first to go corporations cut budgets.” (Friedman cites a company or two who provide support for lifelong learning. If you work for one that does, make sure that you grab every opportunity to learn that you can.)

As most graduate students can attest, when they begin thinking about dissertations, being a good student is not necessarily an indicator of being creative and self-directed.. Most of us need develop additional skills in learning to learn if we’re going to be successful at managing our own careers.

Bearing that in mind, the next posts will cover three essential tools of self-directed adult learning: the learning journal, the learning project and the learning contract.

Stay tuned for more information.

1 For more on the “gig economy” you might want to take a look at this report.
2 A more comprehensive analysis of the the role of platforms in the new economy is Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee and Erik Brynjolfsson, both at MIT.

One of the last pieces that I read, when shutting down for the summer, was a column by Tom Friedman that captured many of the the central themes for our class this fall. (If you want to follow more about the design of the class, you can use the tag EPPL639 on my site. https://generoche.net/category/eppl639/) The core theme of Friedman’s piece is key to the class: an increase in the power of computer technology generated by robotics, artificial intelligence, deep learning and “the Internet of Things,” has created a world where every job worth having demands more skill AND a stronger commitment to self-directed, lifelong learning.

Friedman quotes the C.E.O of Intel about the belief that his grandchildren won’t drive their own cars. History has shown, the danger of putting too much faith in the predictions of the managers of technology companies. Still, it seems increasingly likely that we’ll see fleets of autonomous vehicles in the next couple of decades. Those vehicles may create good jobs for engineers, but doing so will have a massive impact for those who drive for a living, sell liability insurance or own parking lots.

The main catalysts for the move to autonomous vehicles are the exponential increase in power and the decreasing cost of computer power. The next generation of computer chips will allow car manufacturers to shrink the brain of a self-driving car from, “something that fills the whole trunk, to a small box under the front seat”. The result, as Friedman notes, is “a world where we can analyze, prophesize and optimize with a precision unknown in human history”

This ability will transform far more jobs than most of us realize. (We need to constantly remind ourselves that if combined computer capability really does double every year, we’re looking at increases of more than 100 times over the next eight years.) For example, when many of us think of the oil industry, we think of roughnecks, roustabouts and riggers on drilling platforms in the Gulf. That’s only part of the story. As Friedman reminds us in his description of the the control room of Devon Energy in Oklahoma City, a “half a floor of computer screens are displaying the data coming out of every well Devon is drilling around the world…if you’re working on a Devon oil rig today, you’re holding a computer, not just an oily wrench.”

The underlying reality for oil workers is that they will need be be able to use a computer AND a wrench, plus manage their learning in order to keep up with the changes in the world of computing technology. The wrench may not change much, but it’s pretty certain the computer will be telling workers what to adjust and when t should be done.

Friedman also describes one study that showed how reshaping some of our most traditional institutions (like the College Board) can foster self-directed, lifelong learning to the tune of 115 points on the PSAT:

We analyzed 250,000 students from the high school graduating class of 2017 who took the new PSAT and then the new SAT,” College Board president David Coleman told me. “Students who took advantage of their PSAT results to launch their own free personalized improvement practice through Khan Academy advanced dramatically: 20 hours of practice was associated with an average 115-point increase from the PSAT to the SAT — double the average gain among students who did not…

Practice advances all students without respect to high school G.P.A., gender, race and ethnicity or parental education. And it’s free. Our aim is to transform the SAT into an invitation for students to own their future.

As we move forward with our EPPL 639 course this fall, we’ll look at how the lessons of industries, as diverse as oil drilling and long-haul trucking, can shape our personal and professional approaches to learning. For me, Friedman gets it right when he notes that, “And that means: More is now on you. And that means self-motivation to learn and keep learning becomes the most important life skill.”

As I wrote in my first post for our academic information services newsletter, anyone truying to bring about innovation and change in education is in the prediction business—like it or not. Unfortunately, as Yogi Berra supposedly[1] said, “Prediction is difficult, especially about the future”. When we make a decision about implementing a policy, buying some new hardware or software or launching a new program, we’re making a judgement based on our understanding the present and on our imagination of how that present condition might change in the future. Educational planning always involves someone’s interpretation of the future along with some judgment about what steps are most likely to influence our position in that future.

Unfortunately, must of us not very good at either accurately understanding what’s happening now or at anticipating what might happen in the future. Our goal in this class is to help you develop a effective set of tools and techniques that you can use individually and within your organization to better measure where you are and to envision possible futures.

We seem to be particularly bad when we’re focusing on the impact of emerging technologies as diverse as cars, computers or tech corporations:

1903: “The horse is here to stay but the automobile is only a novelty — a fad.” — president of the Michigan Savings Bank advising Henry Ford’s lawyer not to invest in the Ford Motor Company.

1977: “There is no reason for any individual to have a computer in his home.” — Founder of Digital Equipment Corp Ken Olsen in a speech to the World Future Society.

1996: A Forrester Research analyst(quoted in The New York Times): “Whether they stand alone or are acquired, Apple as we know it is cooked. It’s so classic. It’s so sad.” David Pogue in the New York Times

2016 “It’s very important, if you have something really important, write it out and have it delivered by courier, the old fashioned way. Because, I’ll tell you what, no computer is safe,” President Trump on the importance of cybersecurity to his administration. People Magazine* link

My own record record in making predictions about educational technology is a little sketchy. I wasn’t impressed with wireless the first time I saw it, and when I first saw Youtube and online video, I declared pretty emphatically that wouldn’t work. (I did, however, anticipate the importance of the cable modem. My planning paper at Syracuse University School of Information Studies received a B-. The instructor commented that the the paper met the requirements of the assignment, but that my assertion that coaxial cable would become important in expanding high speed internet to the home was ludicrous.)

As Audrey Watters’ essay about the failures of technology, illustrates, of most of our commercial, not-for-profit and government institutions have been just as bad at making predictions about the future as I have—-maybe even worse. This essay and Audrey’s other writings are worth reading who is serious about educational planning. Her historical perspective is carefully researched and provides some very useful insights about how to frame the future decisions. Even though she’s careful to assert that she is no futurist, I think she is right on target when she identifies the key task for the future of educational institutions.

Therefore the task for schools – and I hope you can start to see where these different predictions start to converge – is to prepare students for a highly technological future, a future that has been almost entirely severed from the systems and processes and practices and institutions of the past.

Clearly, totally severing our connections to the systems, processes, practices and institutions of the past isn’t the role of the university. But it seems clear to me that those of us who work in higher education don’t really have much leverage to change the trajectory of the kinds of technology that will shape our student’s futures. Much of the basic science that will define the commercial technologies of the future will still come from our labs, but Amazon, Apple, Facebook, Google, the Chinese government and even Microsoft will still have more power to define the technologies and workspaces of the future.

As we go forward with this class, we’ll try to practice the art of developing realistic ways of anticipating the future—both individually and institutionally. The key to this technique is to train ourselves to think in terms of questions and possibilities of alternative futures, rather than predicting the success or failure of a particular technology. It makes no sense for us to be arguing about whether of jobs displaced by robots and AI will be 25% or 40%. It does make sense for us to think about how we might respond individually and collectively if large numbers of jobs are replaced by machines. (Maybe the better question is when?)

Before we start to worry, about let’s start with simpler exercise. Everyone in this class is working towards a doctorate, and you must have some expectation of what you might do with that degree once you finish.

What sort of professional and personal goals do you have for your post-dissertation life?

What changes do you predict in the world of work and education that might make it either easier or harder for you to accomplish those goals?

How confident are you that your prediction is accurate? How did you come to come to believe that future might exist?

Are there concrete actions that you can be taking right now that will help you capitalize on the opportunities that technology might make available to you?

Are there actions that you could taking now that will help you overcome any obstacles that technology might through your way?

[1] Nothing is ever as it seems, even finding simple quotes to include in presentations. I grew up thinking that Yogi Berra definitely said this, but it seems that might not be the case. The following comes from the “quote investigator:

In 1991 a marketer in the tourism industry in Virginia ascribed a variant of the saying to Yogi Berra:

Randall Foskey, director of admissions marketing for Colonial Williamsburg, probably said it best last week at the legislative dinner sponsored by the Virginia Hospitality and Travel Association.

“In the words of Yogi Berra, ‘I never make predictions, especially about the future,’” Foskey said.

Our upcoming graduate seminar looking at ways professionals can prepare personally and professionally for a workplace transformed by technology has made the William and Mary News.

Instead of focusing on specific skills, Roche is looking at the bigger picture — how can we design learning experiences that prepare future generations to work together and become problem solvers? This question is among those Roche will explore in his seminar course this fall on Educational Technology Planning.

As the prospectus suggests, we’ll approach that bigger question by answering three smaller questions.

First, how might the dramatic changes in core technologies like machine learning, artificial intelligence and deep learning, “big data”, robotics and “the Internet of Things” shape the workplace of the next generation? If those changes come about, what might it mean to be “college and career ready”?

We’re not going to pretend to be able to predict the future here. As I wrote about in my first post to our Academic Technology blog prediction is a tough business. However, the core process of educational planning includes understanding the complexity of our current environment and then envisioning what the future might become—even when today is complicated and the future is murky.

Second, how can educational institutions at all levels design the technological infrastructures to support learning and effective administration?

The path from the complicated present to the possible future is paved with concrete decisions. Administrators have to decide how to invest in the staff, hardware and software that will prepare their students to navigate the workplace of the future. Those investments are expensive and, once you make the, it’s hard to change directions. We’ll try to find ways during this class to think about how we evaluate and purchase products and service that enhance flexibility and the freedom to learn, while avoiding career-limiting decisions.

Finally, how can individuals become better self-directed learners so that they can thrive personally and professionally in these new work environments?

Even if we can’t predict the jobs of the future or the exact types of education that the next generation of workers will need, it seems pretty likely that the central skill of the future will involve “learning how to learn”. Generations of adult education researchers have identified some of the key components of self-directed learning, and we’ll spend some time in this class looking at how to apply their insights into our own learning.

This is my first post as a Professor of Higher Education in William and Mary’s School of Education. This semester marked the end of a 30 year career as a college administrator and the end of my first full week in my new appointment as a full-time instructional faculty member.

For the last couple of years, I’ve existed on a diet almost exclusively of e-learning — first as Interim Director of University E-Learning Initiatives — then permanently in that position. I’m actually still excited by the enormous potential of Coursera, EdX, Lynda.com and the other innovators in that space. (Online stats courses explain regression equations more clearly than I ever have.) But I believe that really understanding e-learning demands understanding **learning** in all its richness, and it’s been hard in recent years to find the cycles to process the change we’ve been experiencing.

Defining Learning

I used to torture the participants in my adult education classes by locking them in a room for an entire class session with the task of defining “learning.” (I didn’t really lock them in, but they did end up working pretty much the entire three hours.) The white board they filled with definitions just scratched the surface of the richness of that evening’s discussion.

Some of the definitions were cognitive, behavioral, or skill-based; a fair number represented the types of changes that could only be assessed and appreciated by the learner. Some of the stories we told about our own learning captured the transcendent or transformational. We left that room in awe of the complexity of humans and a sense of humility about about abilities our as “educators” to “manage” the process. (Carl Rogers would have been impressed with the work we did on those evenings.)

I’m hoping that my new position will allow me to work on understanding e-learning while reconnecting with my past work on self-directed learning and continuing professional education . I’ll be teaching a full schedule of classes — including a sequence of online and blended courses for a new certificate we’re offering for graduate students and practicing college faculty. Right now I’m working on finalizing the syllabi and learning activities for three one-credit courses: Course Design, Learning Strategies and Technology Enhanced Learning.

In addition, I’m working on several core classes for our EdD programs. One of our goals in the School of Education is to clarify the differences between the EdD degree as focused on practice and the PhD as a degree for scholars and professors. The two courses I’m actively teaching or developing are in Data-Driven Decision-Making and Action Research.

One of my personal goals for this semester is participate in the Connected Courses initiative and to use this forum to reconnect with some of the folks who have helped shape my thinking over the years. I’m going to publish this post with the tag and category of Connected-Course to have a forum to get in the game.